The Advanced Seminar in Winter Semester 2025/26 focuses on Human-AI Interaction in Supply Chain Management. Students can choose between two research streams:
- Behavioral patterns in Human-AI Interaction within Supply Chain related scenarios
Cognitive biases (e.g., pull-to-center, risk aversion) have been a major drawback of decision-making quality in various supply chain management-related scenarios. This topic investigates how these biases may shift when humans interact with generative AI tools (e.g., large language models), and conversely, how biases inherent in Gen-AI agents themselves may influence decision outcomes. The study aims to assess the impact of such behavioral shifts on overall efficiency and performance in specific supply chain scenarios. Participants are expected to conduct a comprehensive literature review, design and execute a small-scale experiment involving Gen-AI agents, and analyze the behavioral outcomes.
- Gen-AI tools in solving Supply Chain Related Optimization Problems
Modern generative AI tools, such as large language models, demonstrate an emerging capability to comprehend and respond to basic operations research (OR) problems—including linear programming and related formulations—even without extensive prompt engineering. This topic explores the feasibility and effectiveness of deploying Gen-AI agents to solve specific supply chain-related OR problems, with a focus on solution quality, interpretability, and scalability. Students will review relevant literature on AI-assisted optimization, implement a Gen-AI-based agent (e.g., using platforms like Dify), and apply it to a well-defined supply chain scenario.
For questions, please contact Till Krieger or Yushuan Zhu.